Atlas-based fiber reconstruction from diffusion tensor MRI data

Purpose

Develop a neural fiber reconstruction method based on diffusion tensor imaging, which is not as sensitive to user-defined regions of interest as streamline tractography.

Methods

A simulated annealing approach is employed to find a non-rigid transformation to map a fiber bundle from a fiber atlas to another fiber bundle, which minimizes a specific energy functional. The energy functional describes how well the transformed fiber bundle fits the patient??s diffusion tensor data.

Results

The feasibility of the method is demonstrated on a diffusion tensor software phantom. We analyze the behavior of the algorithm with respect to image noise and number of iterations. First results on the datasets of patients are presented.

Conclusions

The described method maps fiber bundles based on diffusion tensor data and shows high robustness to image noise. Future developments of the method should help simplify inter-subject comparisons of fiber bundles.     

Atlas-based prostate segmentation using an hybrid registration

Purpose  This paper presents the preliminary results of a semi-automatic method for prostate segmentation of magnetic resonance images (MRI) which aims to be incorporated in a navigation system for prostate brachytherapy. Methods  The method is based on the registration of an anatomical atlas computed from a population of 18 MRI exams onto a patient image. An hybrid registration framework which couples an intensity-based registration with a robust point-matching algorithm is used for both atlas building and atlas registration. Results  The method has been validated on the same dataset that the one used to construct the atlas using the leave-one-out method. Results gives a mean error of 3.39 mm and a standard deviation of 1.95 mm with respect to expert segmentations. Conclusions  We think that this segmentation tool may be a very valuable help to the clinician for routine quantitative image exploitation.     

Automatic fiber bundle segmentation in massive tractography datasets using a multi-subject bundle atlas

This paper presents a method for automatic segmentation of white matter fiber bundles from massive dMRI tractography datasets. The method is based on a multi-subject bundle atlas derived from a two-level intra-subject and inter-subject clustering strategy. This atlas is a model of the brain white matter organization, computed for a group of subjects, made up of a set of generic fiber bundles that can be detected in most of the population. Each atlas bundle corresponds to several inter-subject clusters manually labeled to account for subdivisions of the underlying pathways often presenting large variability across subjects. An atlas bundle is represented by the multi-subject list of the centroids of all intra-subject clusters in order to get a good sampling of the shape and localization variability. The atlas, composed of 36 known deep white matter bundles and 47 superficial white matter bundles in each hemisphere, was inferred from a first database of 12 brains. It was successfully used to segment the deep white matter bundles in a second database of 20 brains and most of the superficial white matter bundles in 10 subjects of the same database.     

Atlas-based segmentation of white matter tracts of the human brain using diffusion tensor tractography and comparison with classical dissection

The technique of diffusion tensor tractography is gaining increasing prominence as a non-invasive method for studying the architecture of the white matter pathways in the human brain. Numerous studies have been published that attempt to identify or reconstruct particular pathways of interest. An atlas or map of all the pathways in the white matter would be particularly useful for providing detailed anatomical data that is not available in studies based on conventional MRI data. In this paper we present a method for constructing a white matter atlas to define structures from diffusion tensor tractography by making use of the locations of the anatomical terminations of individual streamlines that pass through white matter. We show how a map of unique seed regions can be used to generate tracts of interest. This approach provides anatomical information that can be rapidly applied to MRI datasets for the clear identification of white matter tracts. We show close correspondence of the tracts generated from the atlas with tracts isolated with classical dissection of post-mortem brain tissue.     

Atlas-based hippocampus segmentation in Alzheimer's disease and mild cognitive impairment

This study assesses the performance of public-domain automated methodologies for MRI-based segmentation of the hippocampus in elderly subjects with Alzheimer's disease (AD) and mild cognitive impairment (MCI). Structural MR images of 54 age- and gender-matched healthy elderly individuals, subjects with probable AD, and subjects with MCI were collected at the University of Pittsburgh Alzheimer's Disease Research Center. Hippocampi in subject images were automatically segmented by using AIR, SPM, FLIRT, and the fully deformable method of Chen to align the images to the Harvard atlas, MNI atlas, and randomly selected, manually labeled subject images ("cohort atlases"). Mixed-effects statistical models analyzed the effects of side of the brain, disease state, registration method, choice of atlas, and manual tracing protocol on the spatial overlap between automated segmentations and expert manual segmentations. Registration methods that produced higher degrees of geometric deformation produced automated segmentations with higher agreement with manual segmentations. Side of the brain, presence of AD, choice of reference image, and manual tracing protocol were also significant factors contributing to automated segmentation performance. Fully automated techniques can be competitive with human raters on this difficult segmentation task, but a rigorous statistical analysis shows that a variety of methodological factors must be carefully considered to insure that automated methods perform well in practice. The use of fully deformable registration methods, cohort atlases, and user-defined manual tracings are recommended for highest performance in fully automated hippocampus segmentation.     

A rotation-invariant spherical harmonic decomposition method for mapping intravoxel multiple fiber structures

A new rotation-invariant spherical harmonic decomposition (SHD) method is proposed in this paper for analyzing high angular resolution diffusion (HARD) imaging. Regular SHD methods have been used to characterize the features of the apparent diffusion coefficient (ADC) profile measured by the HARD technique. However, these regular SHD methods are rotation-variant, i.e., the magnitude and/or the phase of the harmonic components changes with the rotation of the ADC profile. We propose a new rotation-invariant SHD (RI-SHD) method based on the rotation-invariant property of a diffusion tensor model. The basic idea of the proposed method is to reorient the measured ADC profile into a local coordinate system determined by the three eigenvectors of the diffusion tensor in each imaging voxel, and then apply a SHD to the ADC profile. Both simulations and in vivo experiments were carried out to validate the method. Comparisons were made between the component maps from a regular SHD method, diffusion circular spectrum mapping (DCSM) method and the proposed RI-SHD method. The results indicate that the regular SHD maps vary significantly with the rotation of the diffusion-encoding scheme, whereas the maps of the DCSM and the proposed method remain unchanged. In particular, the (0,0)-th, (2,2)-th and (4,4)-th component maps from the RI-SHD method exhibited good consistency with the 0th, 2nd and 4th order maps of the DCSM method, respectively. Compared with the regular SHD methods used in HARD imaging, the proposed RI-SHD method is superior in characterizing the diffusion patterns of multiple fiber structures between different brain regions or across subjects.     

Flow-based fiber tracking with diffusion tensor and q-ball data: validation and comparison to principal diffusion direction techniques

In this study, we evaluate the performance of a flow-based surface evolution fiber tracking algorithm by means of a physical anisotropic diffusion phantom with known connectivity. We introduce a novel speed function for surface evolution that is derived from either diffusion tensor (DT) data, high angular resolution diffusion (HARD) data, or a combined DT-HARD hybrid approach. We use the model-free q-ball imaging (QBI) approach for HARD reconstruction. The anisotropic diffusion phantom allows us to compare and evaluate the performance of different fiber tracking approaches in the presence of real imaging artifacts, noise, and subvoxel partial volume averaging of fiber directions. The surface evolution approach, using the full diffusion tensor as opposed to the principal diffusion direction (PDD) only, is compared to PDD-based line propagation fiber tracking. Additionally, DT reconstruction is compared to HARD reconstruction for fiber tracking, both using surface evolution. We show the potential for surface evolution using the full diffusion tensor to map connections in regions of subvoxel partial volume averaging of fiber directions, which can be difficult to map with PDD-based methods. We then show that the fiber tracking results can be improved by using high angular resolution reconstruction of the diffusion orientation distribution function in cases where the diffusion tensor model fits the data poorly.     

Atlas-based segmentation of 3D cerebral structures with competitive level sets and fuzzy control

We propose a novel approach for the simultaneous segmentation of multiple structures with competitive level sets driven by fuzzy control. To this end, several contours evolve simultaneously toward previously defined anatomical targets. A fuzzy decision system combines the a priori knowledge provided by an anatomical atlas with the intensity distribution of the image and the relative position of the contours. This combination automatically determines the directional term of the evolution equation of each level set. This leads to a local expansion or contraction of the contours, in order to match the boundaries of their respective targets. Two applications are presented: the segmentation of the brain hemispheres and the cerebellum, and the segmentation of deep internal structures. Experimental results on real magnetic resonance (MR) images are presented, quantitatively assessed and discussed.     

应用弥散张量成像实现神经纤维追踪的方法

背景:目前的神经纤维追踪方法众说纷纭,不同的追踪方法往往对数据的要求比较严格,格式比较单一,并且在特定的条件下,才能进行纤维追踪,但都没有统一可执行标准.因此需要研究一种简便通用的神经纤维追踪处理方法.目的:提出一种简便通用,并且可以在不同的数据采集方法情况下,转变为固定的数据采集格式的弥散张量神经纤维追踪方法.方法;首先采用MRIcro软件在原始DTI图像上,找出梯度因子b值,组成梯度方向文件并把DICOM原始文件软件转换成Analyze格式文件,然后利用SPM软件对格式文件标准化,最后采用DTI-track软件进行神经纤维追踪处理.结果与结论:通过对DTI图像进行处理,证明该方法能有效的得出扩散梯度因子b值文件,并且能把不同的DTI数据转变为统一的格式进行DTI处理,得到预期的DTI纤维追踪图像,为DTI研究提供了一个简便有效的具体实现方法.     

White matter fiber tract segmentation in DT-MRI using geometric flows

In this paper, we present a 3D geometric flow designed to segment the main core of fiber tracts in diffusion tensor magnetic resonance images. The fundamental assumption of our fiber segmentation technique is that adjacent voxels in a tract have similar properties of diffusion. The fiber segmentation is carried out with a front propagation algorithm constructed to fill the whole fiber tract. The front is a 3D surface that evolves with a propagation speed proportional to a measure indicating the similarity of diffusion between the tensors lying on the surface and their neighbors in the direction of propagation. We use a level set implementation to assure a stable and accurate evolution of the surface and to handle changes of topology of the surface during the evolution process. The fiber tract segmentation method does not need a regularized tensor field since the surface is automatically smoothed as it propagates. The smoothing is done by an intrinsic surface force, based on the minimal principal curvature. This segmentation can be used for obtaining quantitative measures of the diffusion in the fiber tracts and it can also be used for white matter registration and for surgical planning.     

Parametric spherical deconvolution: inferring anatomical connectivity using diffusion MR imaging

The human brain forms a complex neural network with a connectional architecture that is still far from being known in full detail, even at the macroscopic level. The advent of diffusion MR imaging has enabled the exploration of the structural properties of white matter in vivo. In this article we propose a new forward model that maps the microscopic geometry of nervous tissue onto the water diffusion process and further onto the measured MR signals. Our spherical deconvolution approach completely parameterizes the fiber orientation density by a finite mixture of Bingham distributions. In addition, we define the term anatomical connectivity, taking the underlying image modality into account. This neurophysiological metric may represent the proportion of the nerve fibers originating in the source area which intersect a given target region. The specified inverse problem is solved by Bayesian statistics. Posterior probability maps denote the probability that the connectivity value exceeds a chosen threshold, conditional upon the noisy observations. These maps allow us to draw inferences about the structural organization of the cerebral cortex. Moreover, we will demonstrate the proposed approach with diffusion-weighted data sets featuring high angular resolution.     

Variational inference of the fiber orientation density using diffusion MR imaging

Diffusion MR imaging has enabled the in vivo exploration of the connectional architecture in human brain. This method particularly reveals the complex system of long-range nerve fibers that integrate the functionally distinct areas of the cerebral cortex. Since the fibers are not directly observed but the diffusion process of water molecules in the underlying material, a forward model is established that maps the microgeometry of nervous tissue onto the diffusion-weighted signals. This article proposes the spherical deconvolution of the fiber orientation density in a reproducing kernel Hilbert space, thereby generalizing previous approaches that perform a truncated Fourier analysis on the sphere. The specified inverse problem is solved within a smoothing spline framework which preserves the characteristic properties of a density function, namely its normalization and non-negativity. A Gaussian process model allows the specification of confidence bands for the estimated fiber orientation density and the rigorous selection of the hyperparameters, here the high-frequency content in the density function and the noise variance of the MR observations. In addition, we weaken the constant diffusivity assumption frequently made in the spherical convolution methodology. The novel approach, which uncovers the fiber orientation field of white matter, is demonstrated with diffusion-weighted data sets featuring high angular resolution.     

Intra-retinal layer segmentation of 3D optical coherence tomography using coarse grained diffusion map

Optical coherence tomography (OCT) is a powerful and noninvasive method for retinal imaging. In this paper, we introduce a fast segmentation method based on a new variant of spectral graph theory named diffusion maps. The research is performed on spectral domain (SD) OCT images depicting macular and optic nerve head appearance. The presented approach does not require edge-based image information in localizing most of boundaries and relies on regional image texture. Consequently, the proposed method demonstrates robustness in situations of low image contrast or poor layer-to-layer image gradients. Diffusion mapping applied to 2D and 3D OCT datasets is composed of two steps, one for partitioning the data into important and less important sections, and another one for localization of internal layers. In the first step, the pixels/voxels are grouped in rectangular/cubic sets to form a graph node. The weights of the graph are calculated based on geometric distances between pixels/voxels and differences of their mean intensity. The first diffusion map clusters the data into three parts, the second of which is the area of interest. The other two sections are eliminated from the remaining calculations. In the second step, the remaining area is subjected to another diffusion map assessment and the internal layers are localized based on their textural similarities. The proposed method was tested on 23 datasets from two patient groups (glaucoma and normals). The mean unsigned border positioning errors (mean ± SD) was 8.52 ± 3.13 and 7.56 ± 2.95 μm for the 2D and 3D methods, respectively.     

磁共振扩散张量成像在显示正常人脑白质纤维中的应用

目的应用DTI技术显示正常人脑白质纤维,探讨其与解剖学描述的一致性。方法20名健康志愿者行颅脑MRI与颅脑单次激发回波平面扩散张量成像扫描(b值=0,500s/mm2),在SiemensLeonardo工作站应用纤维束跟踪软件(SiemensStandar12dirs)进行后处理重建出白质纤维束。结果对主要白质纤维如皮质脊髓束、皮质核束、胼胝体、扣带、上纵束、下纵束、上枕额束、下枕额束、钩束进行模拟显示,不同纤维需要选择适合的感兴趣区、各向异性阈值、角度阈值、步长和体素内采样数目等参数,显示结果与解剖学描述具有较好的一致性。结论利用扩散张量成像技术可模拟显示正常人脑白质纤维,与解剖学描述具有较好的一致性,是在活体中研究人脑白质纤维的一种较可靠的方法。     

Automated fiber tracking of human brain white matter using diffusion tensor imaging

Reconstruction of white matter tracts based on diffusion tensor imaging (DTI) is currently widely used in clinical research. This reconstruction allows us to identify coordinates of specific white matter tracts and to investigate their anatomy. Fiber reconstruction, however, relies on manual identification of anatomical landmarks of a tract of interest, which is based on subjective judgment and thus a potential source of experimental variability. Here, an automated tract reconstruction approach is introduced. A set of reference regions of interest (rROIs) known to select a tract of interest was marked in our DTI brain atlas. The atlas was then linearly transformed to each subject, and the rROI set was transferred to the subject for tract reconstruction. Agreement between the automated and manual approaches was measured for 11 tracts in 10 healthy volunteers and found to be excellent (kappa>0.8) and remained high up to 4-5 mm of the linear transformation errors. As a first example, the automated approach was applied to brain tumor patients and strategies to cope with severe anatomical abnormalities are discussed.     

Functional activation using apparent diffusion coefficient-dependent contrast allows better spatial localization to the neuronal activity: evidence using diffusion tensor imaging and fiber tracking

Recent studies suggested that functional activation using apparent diffusion coefficient (ADC) contrast can be used to detect synchronized functional MRI (fMRI) signal changes during brain activation. Such changes may reflect better spatial localization to the smaller vessels, which are closely coupled to the true neuronal activation. Since it is generally believed that there are neural pathways among neuronally relevant areas, methods that would allow clear delineation of such pathways could help validate the neuronal relevance of the activated functional areas. The development of diffusion tensor imaging (DTI) has shown promise in detailed nerve fiber tracking. In this report, DTI was adopted to track the fiber connections among the discrete areas determined using the ADC contrast, in an effort to confirm the neuronal origin of these activated areas. As a comparison, activated areas using blood oxygenation level-dependent (BOLD) contrast were also obtained. Our results showed that the areas determined by the ADC contrast consistently allowed better fiber tracking within, while the BOLD-activated areas were more spatially diffused due to the smearing effect of brain vasculature, rendering the task of fiber tracking more difficult. This observation provides converging evidence that the activated areas using ADC contrast are more closely coupled to the neuronal activity than those using BOLD contrast.     

Development of a graded index microlens based fiber optical trap and its characterization using principal component analysis

We demonstrate a miniaturized single beam fiber optical trapping probe based on a high numerical aperture graded index (GRIN) micro-objective lens. This enables optical trapping at a distance of 200μm from the probe tip. The fiber trapping probe is characterized experimentally using power spectral density analysis and an original approach based on principal component analysis for accurate particle tracking. Its use for biomedical microscopy is demonstrated through optically mediated immunological synapse formation.OCIS codes: (060.2310) Fiber optics, (110.2760) Gradient-index lenses, (170.0170) Medical optics and biotechnology, (170.3880) Medical and biological imaging, (170.4520) Optical confinement and manipulation, (180.0180) Microscopy, (180.2520) Fluorescence microscopy, (350.4855) Optical tweezers or optical manipulation     

Direct estimation of the fiber orientation density function from diffusion-weighted MRI data using spherical deconvolution

Diffusion-weighted magnetic resonance imaging can provide information related to the arrangement of white matter fibers. The diffusion tensor is the model most commonly used to derive the orientation of the fibers within a voxel. However, this model has been shown to fail in regions containing several fiber populations with distinct orientations. A number of alternative models have been suggested, such as multiple tensor fitting, q-space, and Q-ball imaging. However, each of these has inherent limitations. In this study, we propose a novel method for estimating the fiber orientation distribution directly from high angular resolution diffusion-weighted MR data without the need for prior assumptions regarding the number of fiber populations present. We assume that all white matter fiber bundles in the brain share identical diffusion characteristics, thus implicitly assigning any differences in diffusion anisotropy to partial volume effects. The diffusion-weighted signal attenuation measured over the surface of a sphere can then be expressed as the convolution over the sphere of a response function (the diffusion-weighted attenuation profile for a typical fiber bundle) with the fiber orientation density function (ODF). The fiber ODF (the distribution of fiber orientations within the voxel) can therefore be obtained using spherical deconvolution. The properties of the technique are demonstrated using simulations and on data acquired from a volunteer using a standard 1.5-T clinical scanner. The technique can recover the fiber ODF in regions of multiple fiber crossing and holds promise for applications such as tractography.     

Resolving fiber crossing using advanced fast marching tractography based on diffusion tensor imaging

Magnetic resonance diffusion tensor tractography is a powerful tool for the non-invasive depiction of the white matter architecture in the human brain. However, due to limitations in the underlying tensor model, the technique is often unable to reconstruct correct trajectories in heterogeneous fiber arrangements, such as axonal crossings. A novel tractography method based on fast marching (FM) is proposed which is capable of resolving fiber crossings and also permits trajectories to branch. It detects heterogeneous fiber arrangements by incorporating information from the entire diffusion tensor. The FM speed function is adapted to the local tensor characteristics, allowing in particular to maintain the front evolution direction in crossing situations. In addition, the FM's discretization error is reduced by increasing the number of considered possible front evolution directions. The performance of the technique is demonstrated in artificial data and in the healthy human brain. Comparisons with standard FM tractography and conventional line propagation algorithms show that, in the presence of interfering structures, the proposed method is more accurate in reconstructing trajectories. The in vivo results illustrate that the elucidated major white matter pathways are consistent with known anatomy and that multiple crossings and tract branching are handled correctly.